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<a href="mma__tensor__op__tile__iterator__wmma_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/***************************************************************************************************</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017-2019, NVIDIA CORPORATION.  All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * Redistribution and use in source and binary forms, with or without modification, are permitted</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> * provided that the following conditions are met:</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *     * Redistributions of source code must retain the above copyright notice, this list of</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *       conditions and the following disclaimer.</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> *     * Redistributions in binary form must reproduce the above copyright notice, this list of</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> *       conditions and the following disclaimer in the documentation and/or other materials</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *       provided with the distribution.</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *       to endorse or promote products derived from this software without specific prior written</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *       permission.</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS &quot;AS IS&quot; AND ANY EXPRESS OR</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="comment"> **************************************************************************************************/</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="cutlass_8h.html">cutlass/cutlass.h</a>&quot;</span></div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="wmma_8h.html">cutlass/arch/wmma.h</a>&quot;</span></div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="preprocessor">#if defined(CUTLASS_ARCH_WMMA_ENABLED)</span></div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="wmma__array_8h.html">cutlass/wmma_array.h</a>&quot;</span></div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="numeric__types_8h.html">cutlass/numeric_types.h</a>&quot;</span></div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tensor__ref_8h.html">cutlass/tensor_ref.h</a>&quot;</span></div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="matrix__shape_8h.html">cutlass/matrix_shape.h</a>&quot;</span></div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="memory__sm75_8h.html">cutlass/arch/memory_sm75.h</a>&quot;</span></div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="include_2cutlass_2gemm_2gemm_8h.html">cutlass/gemm/gemm.h</a>&quot;</span></div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="layout_2matrix_8h.html">cutlass/layout/matrix.h</a>&quot;</span></div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tensor_8h.html">cutlass/layout/tensor.h</a>&quot;</span></div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="pitch__linear_8h.html">cutlass/layout/pitch_linear.h</a>&quot;</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tensor__op__multiplicand__sm75_8h.html">cutlass/layout/tensor_op_multiplicand_sm75.h</a>&quot;</span></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="platform_8h.html">cutlass/platform/platform.h</a>&quot;</span></div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="fast__math_8h.html">cutlass/fast_math.h</a>&quot;</span></div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacecutlass.html">cutlass</a> {</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="keyword">namespace </span>gemm {</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;<span class="keyword">namespace </span>warp {</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="keyword">template</span> &lt;</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    <span class="keyword">typename</span> Shape_,</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    <a class="code" href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6">Operand</a> <a class="code" href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6">Operand</a>,</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    <span class="keyword">typename</span> Element_,</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    <span class="keyword">typename</span> Layout_,</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    <span class="keywordtype">int</span> OpDelta_,</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    <span class="keywordtype">int</span> Threads,</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    <span class="keyword">typename</span> Policy_&gt;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;<span class="keyword">class </span>MmaTensorOpWmmaMultiplicandTileIterator;</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;<span class="keyword">template</span> &lt;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    <span class="keyword">typename</span> Shape_,</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    <span class="keyword">typename</span> Element_,</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    <span class="keyword">typename</span> Layout_,</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    <span class="keywordtype">int</span> OpDelta_,    </div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    <span class="keyword">typename</span> Policy_&gt;</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;<span class="keyword">class </span>MmaTensorOpWmmaMultiplicandTileIterator&lt;</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    Shape_, Operand::<a class="code" href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6a30f767aa191cd5d261e767fd78393607">kA</a>, Element_, Layout_,</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    OpDelta_, 32, Policy_&gt; {</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160; <span class="keyword">public</span>:</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;  <span class="keyword">using</span> Shape = Shape_;</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;  <span class="keyword">static</span> Operand <span class="keyword">const</span> kOperand = <a class="code" href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6a30f767aa191cd5d261e767fd78393607">Operand::kA</a>;</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  <span class="keyword">using</span> Element = Element_;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;  <span class="keyword">using</span> Layout = Layout_;</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;  <span class="keyword">static</span> <span class="keywordtype">int</span> <span class="keyword">const</span> kOpDelta = OpDelta_;</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;  <span class="keyword">using</span> Policy = Policy_;</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;  <span class="comment">//</span></div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;  <span class="comment">// Derived quantities</span></div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;  <span class="comment">//</span></div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;<span class="comment"></span>  <span class="keyword">using</span> TensorRef = TensorRef&lt;Element, Layout&gt;;</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;  <span class="keyword">using</span> Index = <span class="keyword">typename</span> <a class="code" href="classcutlass_1_1TensorRef.html#a11ec4b07a2132e647ca2ebe5112ce5ec">TensorRef::Index</a>;</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;  <span class="keyword">using</span> LongIndex = <span class="keyword">typename</span> <a class="code" href="classcutlass_1_1TensorRef.html#adeada5e33b231f125a4aaeaf963bd3a3">TensorRef::LongIndex</a>;</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;  <span class="keyword">using</span> TensorCoord = <span class="keyword">typename</span> <a class="code" href="classcutlass_1_1TensorRef.html#ace218cdb46555a46bd71dbdfc2c317c1">TensorRef::TensorCoord</a>;</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;  <span class="keyword">using</span> WmmaShape = MatrixShape&lt;</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    Policy::Operator::Shape::kM, </div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    Policy::Operator::Shape::kK</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;  &gt;;</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;  <span class="keyword">using</span> WmmaDataType = <span class="keyword">typename</span> cutlass::arch::CutlassToWmmaDataType&lt;Element&gt;::Type;</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;  <span class="keyword">using</span> Iterations = MatrixShape&lt;</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    Shape::kRow / WmmaShape::kRow,</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    1 </div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;  &gt;;</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;  <span class="keyword">using</span> Fragment = WmmaFragmentArray&lt;typename Policy::Operator::FragmentA, Iterations::kCount&gt;;</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;  <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(kOperand == <a class="code" href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6a30f767aa191cd5d261e767fd78393607">Operand::kA</a>,</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    <span class="stringliteral">&quot;MmaTensorOpWmmaMultiplicandTileIterator may only be instantiated for A operands to warp-level Mma.&quot;</span>);</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;  <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    <a class="code" href="structcutlass_1_1platform_1_1integral__constant.html#a9bbaca83ae76941edb9b75b2741d3ad9">platform::is_same&lt;cutlass::layout::RowMajor, Layout&gt;::value</a> ||</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    <a class="code" href="structcutlass_1_1platform_1_1integral__constant.html#a9bbaca83ae76941edb9b75b2741d3ad9">platform::is_same&lt;cutlass::layout::ColumnMajor, Layout&gt;::value</a>,</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    <span class="stringliteral">&quot;Supported list of memory layouts for WMMA are: RowMajor, ColumnMajor&quot;</span>);</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;  <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(kOpDelta == 1,</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    <span class="stringliteral">&quot;Alternative arrangements not supported at present.&quot;</span>);</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;  <span class="keywordtype">char</span> <span class="keyword">const</span> *pointer_;</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;  </div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;  Index byte_offset_;</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;  </div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;  Index stride_;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;  Layout layout_;</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;  </div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator() { }</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator(</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    TensorRef <span class="keyword">const</span> &amp;ref, </div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    <span class="keywordtype">int</span> lane_id</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;  ): pointer_(reinterpret_cast&lt;char const*&gt;(ref.data())), byte_offset_(0), stride_(ref.stride(0)), layout_(ref.stride(0)) { </div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;  </div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;  }</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp;add_pointer_offset(LongIndex offset) {</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    byte_offset_ += (offset * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a>) / 8;</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;  }</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp;add_tile_offset(TensorCoord <span class="keyword">const</span> &amp;tile_offset) {</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    Index elements_offset = layout_({tile_offset.row() * Shape::kRow, tile_offset.column() * WmmaShape::kColumn});</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    </div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    byte_offset_ += (elements_offset * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a>) / 8;</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;  }</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp; <a class="code" href="namespacecutlass.html#a1c7a9e66ca7b5dc7413ea3b8f349530a">operator++</a>() {</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;    </div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    Index elements_offset = layout_({0, WmmaShape::kColumn});</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    byte_offset_ += (elements_offset * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a>) / 8;</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;  }</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp; <a class="code" href="namespacecutlass.html#aeb280e9a234c4bcef9646c0a947f93a5">operator--</a>() {</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    </div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    Index elements_offset = layout_({0, WmmaShape::kColumn});</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    byte_offset_ -= (elements_offset * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a>) / 8;</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;  }</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp; <a class="code" href="namespacecutlass.html#a146088ed2566a2c008f0f7a99a87845b">operator+=</a>(TensorCoord <span class="keyword">const</span> &amp;tile_offset) {</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    add_tile_offset(tile_offset);</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;  }</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp; <a class="code" href="namespacecutlass.html#a4a66575d53215180a9ed2d29b9f39805">operator-=</a>(TensorCoord <span class="keyword">const</span> &amp;tile_offset) {</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    add_tile_offset(-tile_offset);</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;  }</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;  <span class="keywordtype">void</span> load_with_byte_offset(Fragment &amp;frag, Index byte_offset)<span class="keyword"> const </span>{</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k &lt; Iterations::kColumn; ++k) {</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;      <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> m = 0; m &lt; Iterations::kRow; ++m) {</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        Index load_byte_offset = layout_({m * WmmaShape::kRow, k * WmmaShape::kColumn}) * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a> / 8;</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;        <span class="keyword">const</span> WmmaDataType *ptr = <span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span>WmmaDataType *<span class="keyword">&gt;</span>(pointer_ + byte_offset_ + load_byte_offset + byte_offset); </div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;        nvcuda::wmma::load_matrix_sync(frag[m], ptr, stride_); </div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;      </div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;      }</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    }</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;  }</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;  <span class="keywordtype">void</span> load(Fragment &amp;frag)<span class="keyword"> const </span>{</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    load_with_byte_offset(frag, 0);</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;  }</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    </div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;  <span class="keywordtype">void</span> store_with_byte_offset(Fragment <span class="keyword">const</span> &amp;frag, Index byte_offset)<span class="keyword"> const </span>{</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    </div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k &lt; Iterations::kColumn; ++k) {</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;      <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> m = 0; m &lt; Iterations::kRow; ++m) {</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;        Index store_byte_offset = layout_({m * WmmaShape::kRow, k * WmmaShape::kColumn}) * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a> / 8;</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        WmmaDataType *ptr = <span class="keyword">reinterpret_cast&lt;</span>WmmaDataType *<span class="keyword">&gt;</span>(pointer_ + byte_offset_ + store_byte_offset + byte_offset);</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        nvcuda::wmma::store_matrix_sync(ptr, frag[m], stride_); </div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;      </div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;      }</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    }</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;  }</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;  <span class="keywordtype">void</span> store(Fragment <span class="keyword">const</span> &amp;frag)<span class="keyword"> const </span>{</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    store_with_byte_offset(frag, 0);</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;  }</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;  <span class="keywordtype">void</span> set_kgroup_index(<span class="keywordtype">int</span> k_group) {</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <span class="comment">// no operation here</span></div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;  }</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;};</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;<span class="keyword">template</span> &lt;</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    <span class="keyword">typename</span> Shape_,</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    <span class="keyword">typename</span> Element_,</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    <span class="keyword">typename</span> Layout_,</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <span class="keywordtype">int</span> OpDelta_,    </div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    <span class="keyword">typename</span> Policy_&gt;</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;<span class="keyword">class </span>MmaTensorOpWmmaMultiplicandTileIterator&lt;</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    Shape_, Operand::<a class="code" href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6a3e56c011b37f0bc78fb9eb175c1181c6">kB</a>, Element_, Layout_,</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    OpDelta_, 32, Policy_&gt; {</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160; <span class="keyword">public</span>:</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;  <span class="keyword">using</span> Shape = Shape_;</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;  <span class="keyword">static</span> Operand <span class="keyword">const</span> kOperand = <a class="code" href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6a3e56c011b37f0bc78fb9eb175c1181c6">Operand::kB</a>;</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;  <span class="keyword">using</span> Element = Element_;</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;  <span class="keyword">using</span> Layout = Layout_;</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;  <span class="keyword">static</span> <span class="keywordtype">int</span> <span class="keyword">const</span> kOpDelta = OpDelta_;</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;  <span class="keyword">using</span> Policy = Policy_;</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;  <span class="comment">//</span></div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;  <span class="comment">// Derived quantities</span></div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;  <span class="comment">//</span></div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;  <span class="keyword">using</span> TensorRef = TensorRef&lt;Element, Layout&gt;;</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;  <span class="keyword">using</span> Index = <span class="keyword">typename</span> <a class="code" href="classcutlass_1_1TensorRef.html#a11ec4b07a2132e647ca2ebe5112ce5ec">TensorRef::Index</a>;</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;  <span class="keyword">using</span> LongIndex = <span class="keyword">typename</span> <a class="code" href="classcutlass_1_1TensorRef.html#adeada5e33b231f125a4aaeaf963bd3a3">TensorRef::LongIndex</a>;</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;  <span class="keyword">using</span> TensorCoord = <span class="keyword">typename</span> <a class="code" href="classcutlass_1_1TensorRef.html#ace218cdb46555a46bd71dbdfc2c317c1">TensorRef::TensorCoord</a>;</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;  <span class="keyword">using</span> WmmaShape = MatrixShape&lt;</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;    Policy::Operator::Shape::kK, </div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;    Policy::Operator::Shape::kN</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;  &gt;;</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;  <span class="keyword">using</span> WmmaDataType = <span class="keyword">typename</span> cutlass::arch::CutlassToWmmaDataType&lt;Element&gt;::Type;</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;  <span class="keyword">using</span> Iterations = MatrixShape&lt;</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    1,</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    Shape::kColumn / WmmaShape::kColumn</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;  &gt;;</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;  <span class="keyword">using</span> Fragment = WmmaFragmentArray&lt;typename Policy::Operator::FragmentB, Iterations::kCount&gt;;</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;  <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(kOperand == <a class="code" href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6a3e56c011b37f0bc78fb9eb175c1181c6">Operand::kB</a>,</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;    <span class="stringliteral">&quot;MmaTensorOpWmmaMultiplicandTileIterator may only be instantiated for B operands to warp-level Mma.&quot;</span>);</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;  <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    <a class="code" href="structcutlass_1_1platform_1_1integral__constant.html#a9bbaca83ae76941edb9b75b2741d3ad9">platform::is_same&lt;cutlass::layout::RowMajor, Layout&gt;::value</a> ||</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;    <a class="code" href="structcutlass_1_1platform_1_1integral__constant.html#a9bbaca83ae76941edb9b75b2741d3ad9">platform::is_same&lt;cutlass::layout::ColumnMajor, Layout&gt;::value</a>,</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    <span class="stringliteral">&quot;Supported list of memory layouts for WMMA are: RowMajor, ColumnMajor&quot;</span>);</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;  <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(kOpDelta == 1,</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;    <span class="stringliteral">&quot;Alternative arrangements not supported at present.&quot;</span>);</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;  <span class="keywordtype">char</span> <span class="keyword">const</span> *pointer_;</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;  </div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;  Index byte_offset_;</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;  </div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;  Index stride_;</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;  Layout layout_;</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;  </div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator() { }</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator(</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;    TensorRef <span class="keyword">const</span> &amp;ref, </div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    <span class="keywordtype">int</span> lane_id</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;  ): pointer_(reinterpret_cast&lt;char const*&gt;(ref.data())), byte_offset_(0), stride_(ref.stride(0)), layout_(ref.stride(0)) {</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;  }</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp;add_pointer_offset(LongIndex offset) {</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;    </div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    byte_offset_ += (offset * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a>) / 8;</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;  }</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp;add_tile_offset(TensorCoord <span class="keyword">const</span> &amp;tile_offset) {</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;    </div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    Index elements_offset = layout_({tile_offset.row() * WmmaShape::kRow, tile_offset.column() * Shape::kColumn});</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;    </div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;    byte_offset_ += (elements_offset * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a>) / 8;</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;  }</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp; <a class="code" href="namespacecutlass.html#a1c7a9e66ca7b5dc7413ea3b8f349530a">operator++</a>() {</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    </div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;    Index elements_offset = layout_({WmmaShape::kRow, 0});</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;    byte_offset_ += (elements_offset * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a>) / 8;</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    </div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;  }</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp; <a class="code" href="namespacecutlass.html#aeb280e9a234c4bcef9646c0a947f93a5">operator--</a>() {</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    Index elements_offset = layout_({WmmaShape::kRow, 0});</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;    byte_offset_ -= (elements_offset + <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a>) / 8;</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;  }</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp; <a class="code" href="namespacecutlass.html#a146088ed2566a2c008f0f7a99a87845b">operator+=</a>(TensorCoord <span class="keyword">const</span> &amp;tile_offset) {</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;    add_tile_offset(tile_offset);</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;  }</div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;</div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;  MmaTensorOpWmmaMultiplicandTileIterator &amp; <a class="code" href="namespacecutlass.html#a4a66575d53215180a9ed2d29b9f39805">operator-=</a>(TensorCoord <span class="keyword">const</span> &amp;tile_offset) {</div><div 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506</span>&#160;    <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k &lt; Iterations::kRow; ++k) {</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;      <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0; n &lt; Iterations::kColumn; ++n) {</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;        </div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;        Index load_byte_offset = layout_({k * WmmaShape::kRow, n * WmmaShape::kColumn}) * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a> / 8;</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;        <span class="keyword">const</span> WmmaDataType *ptr = <span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span>WmmaDataType *<span class="keyword">&gt;</span>(pointer_ + byte_offset_ + load_byte_offset + byte_offset);</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;        nvcuda::wmma::load_matrix_sync(frag[n], ptr, stride_);        </div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;      }</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;    }</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;  }</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;  <span class="keywordtype">void</span> load(Fragment &amp;frag)<span class="keyword"> const </span>{</div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    load_with_byte_offset(frag, 0);</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;  }</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;    </div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;  <span class="keywordtype">void</span> store_with_byte_offset(Fragment <span class="keyword">const</span> &amp;frag, Index byte_offset)<span class="keyword"> const </span>{</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;    </div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;    <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k &lt; Iterations::kRow; ++k) {</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;      <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0; n &lt; Iterations::kColumn; ++n) {</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;        Index store_byte_offset = layout_({k * WmmaShape::kRow, n * WmmaShape::kColumn}) * <a class="code" href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">sizeof_bits&lt;Element&gt;::value</a> / 8;</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;        WmmaDataType *ptr = <span class="keyword">reinterpret_cast&lt;</span>WmmaDataType *<span class="keyword">&gt;</span>(pointer_ + byte_offset_ + store_byte_offset + byte_offset);</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;        </div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;        nvcuda::wmma::store_matrix_sync(ptr, frag[n], stride_);        </div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;      }</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;    }</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;  }</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;  <span class="keywordtype">void</span> store(Fragment <span class="keyword">const</span> &amp;frag)<span class="keyword"> const </span>{</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;    store_with_byte_offset(frag, 0);</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;  }</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;</div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;  <span class="keywordtype">void</span> set_kgroup_index(<span class="keywordtype">int</span> k_group) {</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;    <span class="comment">// no operation here</span></div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;  }</div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;};</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;</div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;<span class="keyword">template</span> &lt;</div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;    <span class="keyword">typename</span> Shape_,</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;    <span class="keyword">typename</span> Element_,</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;    <span class="keyword">typename</span> Layout_,</div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;    <span class="keyword">typename</span> OpDelta_,</div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;    <span class="keyword">typename</span> Policy_&gt;</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;<span class="keyword">class </span>MmaTensorOpWmmaAccumulatorTileIterator;</div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;</div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;<span class="keyword">template</span> &lt;</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;    <span class="keyword">typename</span> Shape_,</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;    <span class="keyword">typename</span> Element_,</div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;    <span class="keyword">typename</span> Layout_,</div><div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    <span class="keyword">typename</span> OpDelta_,    </div><div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;    <span class="keyword">typename</span> Policy_&gt;</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;<span class="keyword">class </span>MmaTensorOpWmmaAccumulatorTileIterator</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;{</div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160; <span class="keyword">public</span>:</div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;</div><div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;  <span class="keyword">using</span> Shape = Shape_;</div><div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;</div><div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;  <span class="keyword">using</span> Element = Element_;</div><div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;</div><div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;  <span class="keyword">using</span> Layout = Layout_;</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;</div><div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;  <span class="keyword">using</span> OpDelta = OpDelta_;</div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;  <span class="keyword">static</span> <span class="keywordtype">int</span> <span class="keyword">const</span> kThreads = 32;</div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;</div><div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;  <span class="keyword">using</span> Policy = Policy_;</div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;</div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;  <span class="comment">//</span></div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;  <span class="comment">// Derived quantities</span></div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;  <span class="comment">//</span></div><div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;<span class="comment"></span>  <span class="keyword">using</span> TensorRef = TensorRef&lt;Element, Layout&gt;;</div><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;  <span class="keyword">using</span> Index = <span class="keyword">typename</span> <a class="code" href="classcutlass_1_1TensorRef.html#a11ec4b07a2132e647ca2ebe5112ce5ec">TensorRef::Index</a>;</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;  <span class="keyword">using</span> LongIndex = <span class="keyword">typename</span> <a class="code" href="classcutlass_1_1TensorRef.html#adeada5e33b231f125a4aaeaf963bd3a3">TensorRef::LongIndex</a>;</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;  <span class="keyword">using</span> TensorCoord = <span class="keyword">typename</span> <a class="code" href="classcutlass_1_1TensorRef.html#ace218cdb46555a46bd71dbdfc2c317c1">TensorRef::TensorCoord</a>;</div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;</div><div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;  <span class="keyword">using</span> WmmaShape = MatrixShape&lt;</div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;    Policy::Operator::Shape::kM, </div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;    Policy::Operator::Shape::kN</div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;  &gt;;</div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;  </div><div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;  <span class="keyword">using</span> WmmaDataType = <span class="keyword">typename</span> cutlass::arch::CutlassToWmmaDataType&lt;Element&gt;::Type;</div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;  <span class="keyword">static</span> nvcuda::wmma::layout_t <span class="keyword">const</span> WmmaLayout = cutlass::arch::CutlassToWmmaLayout&lt;Layout&gt;::value;</div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;  <span class="keyword">using</span> Iterations = MatrixShape&lt;</div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;    Shape::kRow / WmmaShape::kRow,</div><div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;    Shape::kColumn / WmmaShape::kColumn</div><div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;  &gt;;</div><div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;</div><div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;  <span class="keyword">using</span> Fragment = WmmaFragmentArray&lt;typename Policy::Operator::FragmentC, Iterations::kCount&gt;;</div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;</div><div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;  <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(</div><div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;    <a class="code" href="structcutlass_1_1platform_1_1integral__constant.html#a9bbaca83ae76941edb9b75b2741d3ad9">platform::is_same&lt;cutlass::layout::RowMajor, Layout&gt;::value</a> ||</div><div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;    <a class="code" href="structcutlass_1_1platform_1_1integral__constant.html#a9bbaca83ae76941edb9b75b2741d3ad9">platform::is_same&lt;cutlass::layout::ColumnMajor, Layout&gt;::value</a>,</div><div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;    <span class="stringliteral">&quot;Supported list of memory layouts for WMMA are: RowMajor, ColumnMajor&quot;</span>);</div><div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;</div><div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;  </div><div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;  <a class="code" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt;Element, Layout&gt;</a> ref_;</div><div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;</div><div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;  </div><div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;  MmaTensorOpWmmaAccumulatorTileIterator() { }</div><div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;</div><div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;  MmaTensorOpWmmaAccumulatorTileIterator(</div><div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;    TensorRef <span class="keyword">const</span> &amp;ref, </div><div 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name="l00696"></a><span class="lineno">  696</span>&#160;  }</div><div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;</div><div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;  MmaTensorOpWmmaAccumulatorTileIterator &amp; <a class="code" href="namespacecutlass.html#a1c7a9e66ca7b5dc7413ea3b8f349530a">operator++</a>() {</div><div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;    ref_.<a class="code" href="classcutlass_1_1TensorRef.html#a4bed879c428963070de8ffbdc5d6e4f9">add_coord_offset</a>({Shape::kRow, 0});</div><div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;  }</div><div class="line"><a name="l00704"></a><span 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711</span>&#160;</div><div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;  MmaTensorOpWmmaAccumulatorTileIterator &amp; <a class="code" href="namespacecutlass.html#a146088ed2566a2c008f0f7a99a87845b">operator+=</a>(TensorCoord <span class="keyword">const</span> &amp;tile_offset) {</div><div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;    add_tile_offset(tile_offset);</div><div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;  }</div><div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;</div><div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;  MmaTensorOpWmmaAccumulatorTileIterator &amp; <a class="code" href="namespacecutlass.html#a4a66575d53215180a9ed2d29b9f39805">operator-=</a>(TensorCoord <span class="keyword">const</span> &amp;tile_offset) {</div><div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;    add_tile_offset(-tile_offset);</div><div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;    <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;  }</div><div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;</div><div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;  <span class="keywordtype">void</span> load_with_pointer_offset(Fragment &amp;frag, Index pointer_offset)<span class="keyword"> const </span>{</div><div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;    </div><div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;    <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> m = 0; m &lt; Iterations::kRow; ++m) {</div><div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;      <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0; n &lt; Iterations::kColumn; ++n) {</div><div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;</div><div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;        <span class="keyword">const</span> WmmaDataType * ptr = <span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span>WmmaDataType*<span class="keyword">&gt;</span> (ref_.<a class="code" href="classcutlass_1_1TensorRef.html#ac7db3ca62ab1dfe0d3ea08bcadbc9352">data</a>() + ref_.<a class="code" href="classcutlass_1_1TensorRef.html#a4166ac2a0754574ac21d5d57d74f34e5">offset</a>({m * WmmaShape::kRow, n * WmmaShape::kColumn}) + pointer_offset);</div><div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;        </div><div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;        nvcuda::wmma::load_matrix_sync(frag[m * Iterations::kColumn + n], ptr, ref_.<a class="code" href="classcutlass_1_1TensorRef.html#a191e88bc0fb310be655d700e937ab97c">stride</a>()[0], WmmaLayout); </div><div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;</div><div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;      }</div><div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;    }</div><div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;  }</div><div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;  <span class="keywordtype">void</span> load(Fragment &amp;frag)<span class="keyword"> const </span>{</div><div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;    load_with_pointer_offset(frag, 0);</div><div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;  }</div><div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;    </div><div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;  <span class="keywordtype">void</span> store_with_pointer_offset(Fragment <span class="keyword">const</span> &amp;frag, Index pointer_offset)<span class="keyword"> const </span>{</div><div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;    </div><div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;    <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> m = 0; m &lt; Iterations::kRow; ++m) {</div><div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;      <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> n = 0; n &lt; Iterations::kColumn; ++n) {</div><div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;</div><div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;        WmmaDataType * ptr = <span class="keyword">reinterpret_cast&lt;</span>WmmaDataType*<span class="keyword">&gt;</span> (ref_.<a class="code" href="classcutlass_1_1TensorRef.html#ac7db3ca62ab1dfe0d3ea08bcadbc9352">data</a>() + ref_.<a class="code" href="classcutlass_1_1TensorRef.html#a4166ac2a0754574ac21d5d57d74f34e5">offset</a>({m * WmmaShape::kRow, n * WmmaShape::kColumn}) + pointer_offset);</div><div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;</div><div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;        nvcuda::wmma::store_matrix_sync(ptr, frag[m * Iterations::kColumn + n], ref_.<a class="code" href="classcutlass_1_1TensorRef.html#a191e88bc0fb310be655d700e937ab97c">stride</a>()[0], WmmaLayout); </div><div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;      }</div><div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160;    }</div><div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;  }</div><div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;</div><div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;  <span class="keywordtype">void</span> store(Fragment <span class="keyword">const</span> &amp;frag)<span class="keyword"> const </span>{</div><div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;    store_with_pointer_offset(frag, 0);</div><div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160;  }</div><div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;</div><div class="line"><a name="l00777"></a><span class="lineno">  777</span>&#160;  CUTLASS_DEVICE</div><div class="line"><a name="l00778"></a><span class="lineno">  778</span>&#160;  <span class="keywordtype">void</span> set_kgroup_index(<span class="keywordtype">int</span> k_group) {</div><div class="line"><a name="l00779"></a><span class="lineno">  779</span>&#160;    <span class="comment">// no operation here</span></div><div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160;  }</div><div class="line"><a name="l00781"></a><span class="lineno">  781</span>&#160;};</div><div class="line"><a name="l00782"></a><span class="lineno">  782</span>&#160;</div><div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;</div><div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;</div><div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;} <span class="comment">// namespace warp</span></div><div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;} <span class="comment">// namespace gemm</span></div><div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;} <span class="comment">// namespace cutlass</span></div><div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;</div><div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;</div><div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160;<span class="preprocessor">#endif // if defined(CUTLASS_ARCH_WMMA_ENABLED)</span></div><div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160;</div><div class="line"><a name="l00793"></a><span class="lineno">  793</span>&#160;</div><div class="ttc" id="structcutlass_1_1platform_1_1integral__constant_html_a9bbaca83ae76941edb9b75b2741d3ad9"><div class="ttname"><a href="structcutlass_1_1platform_1_1integral__constant.html#a9bbaca83ae76941edb9b75b2741d3ad9">cutlass::platform::integral_constant::value</a></div><div class="ttdeci">static const value_t value</div><div class="ttdef"><b>Definition:</b> platform.h:261</div></div>
<div class="ttc" id="wmma__array_8h_html"><div class="ttname"><a href="wmma__array_8h.html">wmma_array.h</a></div><div class="ttdoc">Statically sized array of elements that accommodates all CUTLASS-supported numeric types and is safe ...</div></div>
<div class="ttc" id="namespacecutlass_html"><div class="ttname"><a href="namespacecutlass.html">cutlass</a></div><div class="ttdef"><b>Definition:</b> aligned_buffer.h:35</div></div>
<div class="ttc" id="structcutlass_1_1sizeof__bits_html_aff47de86de21dae23ad36184c3d2bb12"><div class="ttname"><a href="structcutlass_1_1sizeof__bits.html#aff47de86de21dae23ad36184c3d2bb12">cutlass::sizeof_bits::value</a></div><div class="ttdeci">static int const value</div><div class="ttdef"><b>Definition:</b> numeric_types.h:43</div></div>
<div class="ttc" id="tensor__ref_8h_html"><div class="ttname"><a href="tensor__ref_8h.html">tensor_ref.h</a></div><div class="ttdoc">Defines a structure containing strides, bounds, and a pointer to tensor data. </div></div>
<div class="ttc" id="tensor__op__multiplicand__sm75_8h_html"><div class="ttname"><a href="tensor__op__multiplicand__sm75_8h.html">tensor_op_multiplicand_sm75.h</a></div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html_ac7db3ca62ab1dfe0d3ea08bcadbc9352"><div class="ttname"><a href="classcutlass_1_1TensorRef.html#ac7db3ca62ab1dfe0d3ea08bcadbc9352">cutlass::TensorRef::data</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE Element * data() const </div><div class="ttdoc">Returns the pointer to referenced data. </div><div class="ttdef"><b>Definition:</b> tensor_ref.h:254</div></div>
<div class="ttc" id="namespacecutlass_1_1gemm_html_a34338284023da7403c9ecbd3f406b2a6"><div class="ttname"><a href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6">cutlass::gemm::Operand</a></div><div class="ttdeci">Operand</div><div class="ttdoc">GEMM operand enumeration: D = A * B + C. </div><div class="ttdef"><b>Definition:</b> include/cutlass/gemm/gemm.h:39</div></div>
<div class="ttc" id="memory__sm75_8h_html"><div class="ttname"><a href="memory__sm75_8h.html">memory_sm75.h</a></div><div class="ttdoc">Architecture-specific operators on memory added for SM75. </div></div>
<div class="ttc" id="include_2cutlass_2gemm_2gemm_8h_html"><div class="ttname"><a href="include_2cutlass_2gemm_2gemm_8h.html">gemm.h</a></div><div class="ttdoc">Defines common types used for all GEMM-like operators. </div></div>
<div class="ttc" id="platform_8h_html"><div class="ttname"><a href="platform_8h.html">platform.h</a></div><div class="ttdoc">C++ features that may be otherwise unimplemented for CUDA device functions. </div></div>
<div class="ttc" id="namespacecutlass_html_a146088ed2566a2c008f0f7a99a87845b"><div class="ttname"><a href="namespacecutlass.html#a146088ed2566a2c008f0f7a99a87845b">cutlass::operator+=</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE half_t &amp; operator+=(half_t &amp;lhs, half_t const &amp;rhs)</div><div class="ttdef"><b>Definition:</b> half.h:654</div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html_a4bed879c428963070de8ffbdc5d6e4f9"><div class="ttname"><a href="classcutlass_1_1TensorRef.html#a4bed879c428963070de8ffbdc5d6e4f9">cutlass::TensorRef::add_coord_offset</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE TensorRef &amp; add_coord_offset(TensorCoord const &amp;coord)</div><div class="ttdoc">Adds an offset to each pointer. </div><div class="ttdef"><b>Definition:</b> tensor_ref.h:326</div></div>
<div class="ttc" id="cutlass_8h_html_a4b1c9f25ab6eaa25e1f2258dd63e6ce4"><div class="ttname"><a href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="ttdeci">#define CUTLASS_PRAGMA_UNROLL</div><div class="ttdef"><b>Definition:</b> cutlass.h:110</div></div>
<div class="ttc" id="namespacecutlass_html_a4a66575d53215180a9ed2d29b9f39805"><div class="ttname"><a href="namespacecutlass.html#a4a66575d53215180a9ed2d29b9f39805">cutlass::operator-=</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE half_t &amp; operator-=(half_t &amp;lhs, half_t const &amp;rhs)</div><div class="ttdef"><b>Definition:</b> half.h:664</div></div>
<div class="ttc" id="tensor_8h_html"><div class="ttname"><a href="tensor_8h.html">tensor.h</a></div><div class="ttdoc">Defines layout functions used by TensorRef and derived classes for common 4-D and 5-D tensor formats...</div></div>
<div class="ttc" id="namespacecutlass_html_a1c7a9e66ca7b5dc7413ea3b8f349530a"><div class="ttname"><a href="namespacecutlass.html#a1c7a9e66ca7b5dc7413ea3b8f349530a">cutlass::operator++</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE half_t &amp; operator++(half_t &amp;lhs)</div><div class="ttdef"><b>Definition:</b> half.h:694</div></div>
<div class="ttc" id="namespacecutlass_1_1gemm_html_a34338284023da7403c9ecbd3f406b2a6a30f767aa191cd5d261e767fd78393607"><div class="ttname"><a href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6a30f767aa191cd5d261e767fd78393607">cutlass::gemm::Operand::kA</a></div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html_a191e88bc0fb310be655d700e937ab97c"><div class="ttname"><a href="classcutlass_1_1TensorRef.html#a191e88bc0fb310be655d700e937ab97c">cutlass::TensorRef::stride</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE Stride stride() const </div><div class="ttdoc">Returns the layout object&amp;#39;s stride vector. </div><div class="ttdef"><b>Definition:</b> tensor_ref.h:277</div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html_ace218cdb46555a46bd71dbdfc2c317c1"><div class="ttname"><a href="classcutlass_1_1TensorRef.html#ace218cdb46555a46bd71dbdfc2c317c1">cutlass::TensorRef::TensorCoord</a></div><div class="ttdeci">typename Layout::TensorCoord TensorCoord</div><div class="ttdoc">Coordinate in logical tensor space. </div><div class="ttdef"><b>Definition:</b> tensor_ref.h:171</div></div>
<div class="ttc" id="matrix__shape_8h_html"><div class="ttname"><a href="matrix__shape_8h.html">matrix_shape.h</a></div><div class="ttdoc">Defines a Shape template for matrix tiles. </div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html"><div class="ttname"><a href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element, Layout &gt;</a></div></div>
<div class="ttc" id="namespacecutlass_html_aeb280e9a234c4bcef9646c0a947f93a5"><div class="ttname"><a href="namespacecutlass.html#aeb280e9a234c4bcef9646c0a947f93a5">cutlass::operator--</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE half_t &amp; operator--(half_t &amp;lhs)</div><div class="ttdef"><b>Definition:</b> half.h:706</div></div>
<div class="ttc" id="cutlass_8h_html_a28c2443a142676d3d71effdae1a986b1"><div class="ttname"><a href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="ttdeci">#define CUTLASS_HOST_DEVICE</div><div class="ttdef"><b>Definition:</b> cutlass.h:89</div></div>
<div class="ttc" id="numeric__types_8h_html"><div class="ttname"><a href="numeric__types_8h.html">numeric_types.h</a></div><div class="ttdoc">Top-level include for all CUTLASS numeric types. </div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html_a4166ac2a0754574ac21d5d57d74f34e5"><div class="ttname"><a href="classcutlass_1_1TensorRef.html#a4166ac2a0754574ac21d5d57d74f34e5">cutlass::TensorRef::offset</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE LongIndex offset(TensorCoord const &amp;coord) const </div><div class="ttdoc">Computes the offset of an index from the origin of the tensor. </div><div class="ttdef"><b>Definition:</b> tensor_ref.h:301</div></div>
<div class="ttc" id="platform_8h_html_adde4c9ea91b753491851361a4198c009"><div class="ttname"><a href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a></div><div class="ttdeci">#define static_assert(__e, __m)</div><div class="ttdef"><b>Definition:</b> platform.h:153</div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html_a11ec4b07a2132e647ca2ebe5112ce5ec"><div class="ttname"><a href="classcutlass_1_1TensorRef.html#a11ec4b07a2132e647ca2ebe5112ce5ec">cutlass::TensorRef::Index</a></div><div class="ttdeci">typename Layout::Index Index</div><div class="ttdoc">Index type. </div><div class="ttdef"><b>Definition:</b> tensor_ref.h:165</div></div>
<div class="ttc" id="layout_2matrix_8h_html"><div class="ttname"><a href="layout_2matrix_8h.html">matrix.h</a></div><div class="ttdoc">Defines layout functions used by TensorRef and derived classes. </div></div>
<div class="ttc" id="fast__math_8h_html"><div class="ttname"><a href="fast__math_8h.html">fast_math.h</a></div><div class="ttdoc">Math utilities. </div></div>
<div class="ttc" id="pitch__linear_8h_html"><div class="ttname"><a href="pitch__linear_8h.html">pitch_linear.h</a></div><div class="ttdoc">Defines layout functions used by TensorRef and derived classes for pitch-linear memory. </div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html_a6bbcd0e512915565cabfeccdb1b6417d"><div class="ttname"><a href="classcutlass_1_1TensorRef.html#a6bbcd0e512915565cabfeccdb1b6417d">cutlass::TensorRef::add_pointer_offset</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE TensorRef &amp; add_pointer_offset(LongIndex offset_)</div><div class="ttdoc">Adds an offset to each pointer. </div><div class="ttdef"><b>Definition:</b> tensor_ref.h:319</div></div>
<div class="ttc" id="namespacecutlass_1_1gemm_html_a34338284023da7403c9ecbd3f406b2a6a3e56c011b37f0bc78fb9eb175c1181c6"><div class="ttname"><a href="namespacecutlass_1_1gemm.html#a34338284023da7403c9ecbd3f406b2a6a3e56c011b37f0bc78fb9eb175c1181c6">cutlass::gemm::Operand::kB</a></div><div class="ttdoc">A multiplicand. </div></div>
<div class="ttc" id="wmma_8h_html"><div class="ttname"><a href="wmma_8h.html">wmma.h</a></div><div class="ttdoc">Templates exposing architecture support for warp matrix multiply-add (WMMA) operations. </div></div>
<div class="ttc" id="cutlass_8h_html"><div class="ttname"><a href="cutlass_8h.html">cutlass.h</a></div><div class="ttdoc">Basic include for CUTLASS. </div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html_adeada5e33b231f125a4aaeaf963bd3a3"><div class="ttname"><a href="classcutlass_1_1TensorRef.html#adeada5e33b231f125a4aaeaf963bd3a3">cutlass::TensorRef::LongIndex</a></div><div class="ttdeci">typename Layout::LongIndex LongIndex</div><div class="ttdoc">Long index used for pointer offsets. </div><div class="ttdef"><b>Definition:</b> tensor_ref.h:168</div></div>
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